Bootstrapping

Bootstrapping is random sampling with replacement. It implies we extract random samples from original dataset ‘n’ times and form a new dataset. With replacement implies same sample extracted is thrown back and can extracted back. The mean calculated for means in resampled data.

For the classification model and algorithm is sensitive to a priori class distribution then we can bootstrap on of classes and reduce bias of model.

It also reduces the effect of noise in the model.